Abstract 6569: ClinGen Somatic and CIViC collaborate to comprehensively evaluate somatic variants in cancer
Bibliographic record
Abstract
Abstract The comprehensive evaluation of somatic variants in cancer requires consensus interpretation of their potential clinical significance (diagnosis, prognosis, and treatment response) and oncogenicity. To aid precision medicine through public interpretations, a multifaceted collaborative effort is required to bring together a community, structured guidance, and a public platform. The over 200 multi-disciplinary experts in the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) provide a community that develops data curation guidelines and standards and curates evidence to determine the clinical significance and oncogenicity of somatic alterations in cancer. The Somatic CDWG established the Pediatric Cancer, Hematological Cancer, and Solid Tumor Taskforces to facilitate membership growth and targeted curation projects. Within these Taskforces, Somatic Cancer Variant Curation Expert Panels (SC-VCEPs) are developed through a 4-step approval process adapted from ClinGen germline VCEP procedures. Five SC-VCEPs (NTRK-fusions, FGFR variants, FLT3 variants, Histone H3 variants, and BCR::ABL1-like B-ALL alterations) are working through the approval process and one (Established Significance) is in the planning stage. SC-VCEPs tailor AMP/ASCO/CAP [PMID:27993330] and ClinGen/CGC/VICC Oncogenicity [PMID:35101336] guidelines, or create new classification guidance to aid granular assessment of their genomic alterations of interest. The Clinical Interpretations of Variants in Cancer (CIViC) knowledgebase provides the public platform for the Taskforces and SC-VCEPs to display their high-quality somatic cancer variant interpretations. These groups curate evidence from published literature and create Assertions summarizing evidence collected. These Assertions are further disseminated through other resources like ClinVar and the Variant Interpretation for Cancer Consortium (VICC) MetaKB. CIViC supports the application of AMP/ASCO/CAP tiering and levels for clinical significance Assertions. CIViC recently created Oncogenic Assertions, which supports the application of ClinGen/CGC/VICC codes. Three SC-VCEPs (FGFR, FLT3, and Histone H3) are tailoring the ClinGen/CGC/VICC Oncogenicity guidelines to their specific genes and CIViC will enable their ClinGen-approved, modified codes to be applied directly to their Oncogenic Assertions. Also, two SC-VCEPs (NTRK and BCR::ABL1-like) are developing fusion specific oncogenicity classification guidelines, which will be applied to their fusion specific CIViC Oncogenic Assertions. ClinGen Somatic groups have generated 695 CIViC Evidence Items and 33 Assertions from over 400 published papers. These numbers will continue to grow along with the development of more SC-VCEPs. ClinGen Somatic and CIViC foster collaboration, innovation, and most importantly the advancement of precision medicine. Citation Format: Jason Saliba, Arpad Danos, Kilannin Krysiak, Adam Coffman, Susanna Kiwala, Joshua McMichael, Cameron J. Grisdale, Ian King, Shamini Selvarajah, Xinjie Xu, Rashmi Kanagal-Shamanna, Laveniya Satgunaseelan, David Meredith, Mark Evans, Charles G. Mullighan, Yassmine Akkari, Gordana Raca, Angshumoy Roy, Alex H. Wagner, Ramaswamy Govindan, Obi L. Griffith, Malachi Griffith, on behalf of the ClinGen Somatic Clinical Domain Working Group. ClinGen Somatic and CIViC collaborate to comprehensively evaluate somatic variants in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6569.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.047 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".